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In this study, the authors survey students and adults to better understand their basic financial knowledge and money saving skills to measure the extent of knowledge in each group and make comparisons between.

Diabetes is a serious worldwide epidemic that affects a growing portion of the population. While the most common method for testing blood glucose levels involves finger pricking, it is painful and inconvenient for patients. The authors test a non-invasive method to measure glucose levels from diabetic patients, and investigate whether the method is clinically accurate and universally applicable.

Previous studies have found that how a food item is labeled may influence people's liking of it. This study used a cookie taste test to investigate whether people's liking of a dessert item would be swayed by the use of different labels.

The authors test different machine learning algorithms to remove background noise from audio to help people with hearing loss differentiate between important sounds and distracting noise.

The authors use pictures of candy wrappers and neural networks to improve nutritional accuracy of diet-tracking apps.

In this study the authors looked the trajectories of cosmic rays moving through a dipole field. They found that the trajectories of cosmic rays are determined by a particle's energy and interaction with Earth's B field.

An integrated plant that would generate energy from solar power and provide clean water would help solve multiple sustainability issues. The feasibility of such a plant was investigated by looking at the efficacy of several different modules of such a plant on a small scale.

The authors train a neural network to detect text-based emotions including joy, sadness, anger, fear, love, and surprise.

Pulmonary diseases like lung cancer and valley fever pose serious health challenges, making accurate and rapid diagnostics essential. This study developed a MATLAB-based software tool that uses computer vision techniques to differentiate between these diseases by analyzing features of lung nodules in CT scans, achieving higher precision than traditional methods.

The authors looked at using machine learning to identify skills needed to apply for certain jobs, specifically looking at different techniques to parse apart the text. They found that Bidirectional Encoder Representation of Transforms (BERT) performed best.